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Author Zhang, Junjie ♦ Zhou, Tao ♦ Lu, Huiling ♦ Shi, Hongbin
Source Directory of Open Access Journals (DOAJ)
Content type Text
Publisher Hindawi Limited
File Format HTM / HTML
Date Created 2016-10-03
Copyright Year ©2016
Language English
Subject Domain (in LCC) R
Subject Keyword Medicine
Abstract In order to improve the detection accuracy of pulmonary nodules in CT image, considering two problems of pulmonary nodules detection model, including unreasonable feature structure and nontightness of feature representation, a pulmonary nodules detection algorithm is proposed based on SVM and CT image feature-level fusion with rough sets. Firstly, CT images of pulmonary nodule are analyzed, and 42-dimensional feature components are extracted, including six new 3-dimensional features proposed by this paper and others 2-dimensional and 3-dimensional features. Secondly, these features are reduced for five times with rough set based on feature-level fusion. Thirdly, a grid optimization model is used to optimize the kernel function of support vector machine (SVM), which is used as a classifier to identify pulmonary nodules. Finally, lung CT images of 70 patients with pulmonary nodules are collected as the original samples, which are used to verify the effectiveness and stability of the proposed model by four groups’ comparative experiments. The experimental results show that the effectiveness and stability of the proposed model based on rough set feature-level fusion are improved in some degrees.
ISSN 23146133
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2016-01-01
e-ISSN 23146133
Journal BioMed Research International
Volume Number 2016

Source: Directory of Open Access Journals (DOAJ)